Business and operational improvements from integrating pipeline scheduling and operating systems.

Proceedings Publication Date:

07 May 2020
Presenter
Paul Dickerson
Presenter
Company
Author
Paul Dickerson, Kevin Webb
Part of the proceedings of
Abstract

Pipeline schedules are increasingly becoming more complex as systems aim to squeeze maximum capacity from the networks, and as the intricacy of those networks escalates. Executing those schedules is also a progressively challenging endeavor.  Decisions must be made in near real time about the details and actions of a product movement.  Traditionally the scheduling group publishes a schedule to the operational group who executes the schedule as best they can.  Schedule details are then updated to reflect how the movement were executed.  Finally, books are closed at the commercial level.  However, we can imagine modifying this paradigm where the schedulers and operators are much more integrated in the way they use and share data.  This to the point of being able to schedule based on information derived in real time; while also being able to use planning tools to predict how a specific product movement will progress - all well ahead of real time.

This paper presents an integrated computational technology which enables the request of movements at the commercial level; publishing in a planning application capable of scheduling complex pipeline and terminal networks; providing the opportunity for an operator to test the schedule ahead of time and record the steps necessary to test knowable preconditions and status to acknowledge readiness; automate the product movement in real time; verify execution and finally close notification loops to schedulers and business entities.

Streamlining and integrating the entire value chain will provide greater operational and business efficiencies while uncovering previously unnoticed synergies in the organization.  This technology also has the potential to reduce risks to the operation such as contamination or operational failures whilst improving the accuracy of the pipeline schedule because it is based on up to the minute data.

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